forked from mrq/DL-Art-School
Spsr 8
This commit is contained in:
James Betker
parent
c9a9e5c525
commit
35469f08e2
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@ -458,7 +458,7 @@ class Spsr6(nn.Module):
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val["switch_%i_histogram" % (i,)] = hists[i]
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return val
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# Variant of Spsr7 which uses multiplexer blocks that feed off of a reference embedding. Also computes that embedding.
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# Variant of Spsr6 which uses multiplexer blocks that feed off of a reference embedding. Also computes that embedding.
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class Spsr7(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
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super(Spsr7, self).__init__()
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@ -594,3 +594,132 @@ class Spsr7(nn.Module):
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val["switch_%i_histogram" % (i,)] = hists[i]
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return val
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# Based on Spsr7 but swaps sw2 to the end of the chain. Also re-enables pretransform convs.
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class Spsr8(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
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super(Spsr7, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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# processing the input embedding
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self.reference_embedding = ReferenceImageBranch(nf)
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# switch options
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self.nf = nf
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transformation_filters = nf
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self.transformation_counts = xforms
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multiplx_fn = functools.partial(QueryKeyMultiplexer, transformation_filters, embedding_channels=512, reductions=multiplexer_reductions)
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pretransform_fn = functools.partial(ConvGnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
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transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5),
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transformation_filters, kernel_size=3, depth=3,
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weight_init_factor=.1)
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
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self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
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self.get_g_nopadding = ImageGradientNoPadding()
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self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False, bias=False)
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self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False)
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self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts // 2, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=1, norm=False, activation=True, bias=True)
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self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
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self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
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# Join branch (grad+fea)
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self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
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self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3)
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self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.final_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
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self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
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self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
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self.switches = [self.sw1, self.sw2, self.sw_grad, self.conjoin_sw]
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self.attentions = None
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self.init_temperature = init_temperature
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self.final_temperature_step = 10000
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self.lr = None
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def forward(self, x, ref, ref_center):
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# The attention_maps debugger outputs <x>. Save that here.
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self.lr = x.detach().cpu()
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x_grad = self.get_g_nopadding(x)
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ref_code = self.reference_embedding(ref, ref_center)
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ref_embedding = ref_code.view(-1, self.nf * 8, 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
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x = self.model_fea_conv(x)
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x1 = x
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x1, a1 = self.sw1(x1, True, identity=x, att_in=(x1, ref_embedding))
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x_grad = self.grad_conv(x_grad)
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x_grad_identity = x_grad
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x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1)
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x_grad, a2 = self.sw_grad(x_grad, True, identity=x_grad_identity, att_in=(x_grad, ref_embedding))
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x_grad = self.grad_lr_conv(x_grad)
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x_grad = self.grad_lr_conv2(x_grad)
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x_grad_out = self.upsample_grad(x_grad)
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x_grad_out = self.grad_branch_output_conv(x_grad_out)
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x_out = x1
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x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
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x_out, a3 = self.conjoin_sw(x_out, True, identity=x1, att_in=(x_out, ref_embedding))
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x_out, a4 = self.sw2(x_out, True, identity=x_out, att_in=(x_out, ref_embedding))
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x_out = self.final_lr_conv(x_out)
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x_out = checkpoint(self.upsample, x_out)
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x_out = checkpoint(self.final_hr_conv1, x_out)
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x_out = self.final_hr_conv2(x_out)
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self.attentions = [a1, a2, a3, a4]
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self.grad_fea_std = grad_fea_std.detach().cpu()
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self.fea_grad_std = fea_grad_std.detach().cpu()
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return x_grad_out, x_out
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def set_temperature(self, temp):
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[sw.set_temperature(temp) for sw in self.switches]
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def update_for_step(self, step, experiments_path='.'):
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if self.attentions:
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temp = max(1, 1 + self.init_temperature *
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(self.final_temperature_step - step) / self.final_temperature_step)
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self.set_temperature(temp)
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if step % 500 == 0:
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output_path = os.path.join(experiments_path, "attention_maps")
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prefix = "amap_%i_a%i_%%i.png"
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[save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
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torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
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def get_debug_values(self, step, net_name):
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temp = self.switches[0].switch.temperature
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mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
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means = [i[0] for i in mean_hists]
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hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
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val = {"switch_temperature": temp,
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"grad_branch_feat_intg_std_dev": self.grad_fea_std,
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"conjoin_branch_grad_intg_std_dev": self.fea_grad_std}
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for i in range(len(means)):
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val["switch_%i_specificity" % (i,)] = means[i]
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val["switch_%i_histogram" % (i,)] = hists[i]
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return val
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@ -73,6 +73,11 @@ def define_G(opt, net_key='network_G', scale=None):
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netG = spsr.Spsr7(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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elif which_model == "spsr8":
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = spsr.Spsr8(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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elif which_model == "ssgr1":
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = ssg.SSGr1(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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